System and Method for Proactive Intervention to Reduce High Cost Channel Usage

ABSTRACT

Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.

CROSS-REFERENCE TO RELATED APPLICATION INFORMATION

This is a continuation of U.S. patent application Ser. No. 16/003,668,filed Jun. 8, 2018, which is incorporated herein by reference in itsentirety.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to a method and system forgenerating an intervening message for a remote client device in responseto an anticipated user action.

BACKGROUND

Automated systems for interacting with customers by generating automaticwritten, auditory, or video responses via web and mobile deviceapplication channels are useful ways to provide customers with requestedinformation and perform routine account actions in an expedited,extended hours fashion, without the need to have a large workforce ofcustomer service agents. While helpful, existing systems tend to providea passive approach of waiting until a user reaches out to the system toaddress potential actions taken by the user.

SUMMARY

Embodiments disclosed herein generally relate to a system and method forproactively generating an intervening message for a remote client devicein response to an anticipated user action. A computing system receivesone or more streams of user activity. The one or more streams of useractivity include interaction with a server of an organization via anapplication executing on the remote client device. The computing systeminputs the one or more streams of user activity into a prediction model.The computing system identifies an anticipated user action based on aprediction output from the prediction model. The computing systemdetermines, based on a solution model, a proposed solution to theanticipated user action. The computing system generates an anticipatedmessage to be transmitted to the remote client device of the user. Theanticipated message includes an indication of the proposed solution. Thecomputing system transmits the anticipated message to the remote clientdevice of the user.

In some embodiments, the prediction model is generated based onhistorical communications between the user and the organization via oneor more communication channels.

In some embodiments, identifying the anticipated user action based onthe prediction output from the prediction model includes the computingsystem identifying a most probable action to be taken by the user basedon the one or more streams of user activity.

In some embodiments, transmitting the anticipated message to the remoteclient device of the user includes the computing system identifying afirst communication channel by which the user most frequentlycommunicated with the organization. The computing system identifies asecond communication channel that is lower cost compared to the firstcommunication channel. The computing system transmits the anticipatedmessage to the remote client device of the user via the secondcommunication channel.

In some embodiments, the prediction model is based on prior useractivity with respect to the first communication channel.

In some embodiments, the prediction model is based on prior activity ofadditional users with respect to the first communication channel.

In some embodiments, the anticipated message is an email message thatcomprises one or more proposed actions to be taken.

In some embodiments, the anticipated message is a text message via anautomated chatbot configured for engaging a dialogue with the user.

In some embodiments, the computing system further receives a messagefrom the remote client device. The message includes a request to takefurther action. The computing system generates an event based on therequest. The computing system executes the request.

In some embodiments, the message is received via a same communicationchannel as the anticipated message.

In another embodiment, a method of proactively generating an interveningmessage for a remote client device in response to an anticipated useraction is disclosed herein. A computing system generates a predictionmodel configured to anticipate an inquiry from a user based on userbehavior. The computing system receives one or more streams of useractivity. The one or more streams of user activity include interactionwith a server of an organization via an application executing on theremote client device. The computing system inputs the one or morestreams of user activity into the prediction model. The computing systemidentifies an anticipated user action based on a prediction output fromthe prediction model. The computing system determines, based on asolution model, a proposed solution to the anticipated action. Thecomputing system generates an anticipated message to be transmitted tothe remote client device of the user. The anticipated message includesan indication of the proposed solution. The computing system transmitsthe anticipated message to the remote client device of the user.

In some embodiments, transmitting the anticipated message to the remoteclient device of the user includes the computing system identifying acommunication channel by which the user most frequently communicatedwith the organization. The computing system identifies a furthercommunication channel that is lower cost compared to the communicationchannel. The computing system transmits the anticipated message to theremote client device of the user via the further communication channel.

In some embodiments, the prediction model is based on prior useractivity with respect to the first communication channel.

In some embodiments, the prediction model is based on prior useractivity of additional users with respect to the first communicationchannel.

In some embodiments, identifying the anticipated user action based onthe prediction output from the prediction model includes the computingsystem identifying a most probable action to be taken by the user basedon the one or more streams of user activity.

In some embodiments, the computing system further receives a messagefrom the remote client device. The message includes a request to takefurther action. The computing system generates an event based on therequest. The computing system executes the request.

In some embodiments, the computing system further generates aconfirmation message to be transmitted to the user. The computing systemtransmits the confirmation message to the remote client device.

In some embodiments, the message is received via a same communicationchannel as the anticipated message.

In another embodiment, a system is disclosed herein. The system includesa processor and a memory. The memory has programming instructions storedthereon, which, when executed by the processor, performs an operation.The operation includes receiving one or more streams of user activity.The one or more streams of user activity include interaction with aserver of an organization via an application executing on the remoteclient device. The operation further includes identifying an anticipateduser action based on a prediction output from a prediction model. Theoperation further includes determining, based on a solution model, aproposed solution to the anticipated user action. The operation furtherincludes generating an anticipated message to be transmitted to theremote client device of the user. The anticipated message includes anindication of the proposed solution. The operation further includestransmitting the anticipated message to the remote client device of theuser.

In some embodiments, the prediction model is generated based onhistorical communications between the user and the organization via oneor more communication channels.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this disclosure and are therefore not to beconsidered limiting of its scope, for the disclosure may admit to otherequally effective embodiments.

FIG. 1 is a block diagram illustrating a computing environment,according to one embodiment.

FIG. 2 is a block diagram illustrating a computing environment,according to one embodiment.

FIG. 3 is a block diagram illustrating one or more communicationchannels, according to one embodiment.

FIG. 4 is a flow diagram illustrating a method of generating anintervening message for a remote client device in response to ananticipated user action, according to one embodiment.

FIG. 5A is a block diagram illustrating interaction of one or morecomponents of a computing environment, according to one embodiment.

FIG. 5B is a block diagram illustrating interaction of one or morecomponents of a computing environment, according to one embodiment.

FIG. 6 is a flow diagram illustrating a method of generating anintervening message for a remote client device in response to ananticipated user action, according to one embodiment.

FIG. 7 is a flow diagram illustrating a method of generating anintervening message for a remote client device in response to ananticipated user action, according to one embodiment.

FIG. 8 is a block diagram illustrating a screenshot of a client device,according to one embodiment.

FIG. 9 is a block diagram illustrating one or more screenshots of aclient device, according to one embodiment.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. It is contemplated that elements disclosed in oneembodiment may be beneficially utilized on other embodiments withoutspecific recitation.

DETAILED DESCRIPTION

The present disclosure generally relates to a method and system forgenerating an intervening message for a remote client device in responseto an anticipated user action. One or more techniques disclosed hereinrelate to leveraging individualized user history (as well as history ofother users) to train a prediction model, which may be used toanticipate future actions of the user based on current user activity.For example, through the prediction model, the present system may inputone or more streams of user activity to anticipate a predicted action ofthe user. In a specific example, assume that a user has historicallycalled a customer service number to confirm payment of a credit cardaccount within five days of submitting an online payment. By recognizingthe action of the user submitting payment, and determining, via theprediction model, that the user is likely to call a customer servicenumber to confirm payment within five days, the disclosed system mayanticipate the user's action, and transmit a payment confirmationmessage to the user within this time period.

In some embodiments, the present disclosure may also identify acommunication channel in which a user is likely to use to communicatewith an organization. For example, one or more techniques disclosedherein may parse the identified streams of user activity in accordancewith the communication channel used to contact the organization. Suchcommunication channels may include, for example, a live customer servicechannel, an artificial intelligence customer service channel, emailcorrespondence, text message correspondence, and the like. To improvebandwidth on higher cost communication channels, such as a live customerservice channel, the system described herein may encourage customers touse lower cost channels, by anticipating actions from the user that theuser would historically carry out using a higher cost channel. Byidentifying those actions, the system may be able to address thecustomer's needs through a lower cost channel, depending on thecomplexity of the user's anticipated action.

The term “user” as used herein includes, for example, a person or entitythat owns a computing device or wireless device; a person or entity thatoperates or utilizes a computing device; or a person or entity that isotherwise associated with a computing device or wireless device. It iscontemplated that the term “user” is not intended to be limiting and mayinclude various examples beyond those described.

FIG. 1 is a block diagram illustrating a computing environment 100,according to one embodiment. Computing environment 100 may include aclient device 102, a third party web server 104, and an organizationcomputing system 106 communicating via network 105. Client device 102may be operated by a user (or customer). For example, client device 102may be a mobile device, a tablet, a desktop computer, or any computingsystem having the capabilities described herein. Client device 102 maybelong to or be provided by a customer, or may be borrowed, rented, orshared. Customers may include individuals such as, for example,subscribers, clients, prospective clients, or customers of an entityassociated with organization computing system 106, such as individualswho have obtained, will obtain, or may obtain a product, service, orconsultation from an entity associated with organization computingsystem 106.

Network 105 may be of any suitable type, including individualconnections via the Internet, such as cellular or Wi-Fi networks. Insome embodiments, network 105 may connect terminals, services, andmobile devices using direct connections, such as radio frequencyidentification (RFID), near-field communication (NFC), Bluetooth™,low-energy Bluetooth™ (BLE), Wi-Fi™, ZigBee™, ambient backscattercommunication (ABC) protocols, USB, WAN, or LAN. Because the informationtransmitted may be personal or confidential, security concerns maydictate one or more of these types of connection be encrypted orotherwise secured. In some embodiments, however, the information beingtransmitted may be less personal, and therefore, the network connectionsmay be selected for convenience over security.

Network 105 may include any type of computer networking arrangement usedto exchange data. For example, network 105 may include any type ofcomputer networking arrangement used to exchange information. Forexample, network 105 may be the Internet, a private data network,virtual private network using a public network and/or other suitableconnection(s) that enables components in computing environment 100 tosend and receiving information between the components of environment100.

Third party web server 104 may include a computer system associated withan entity other than the entity associated with organization computingsystem 106 and customers that perform one or more functions associatedwith the organization computing system 106. For example, third party webserver 104 may be directed to a server hosting an electronic mail(email) web application or website.

Client device 102 may include at least an application 120, a shortmessage service (SMS) client 122, and a voice over internet protocol(VoIP) client 122. Application 120 may be representative of a webbrowser that allows access to a website or a stand-alone application.User of client device 102 may access application 120 to accessfunctionality of third party web server 104 (and/or web server 108).User operating client device 102 may communicate over network 105 torequest a webpage, for example, from third party web server 104. Forexample, client device 102 may be configured to execute application 120to access one or more functionalities managed by third party web server104. The content that is displayed to user of client device 102 may betransmitted from third party web server 104 to client device 102, andsubsequently processed by application 120 for display through agraphical user interface (GUI) of client device 102. SMS client 122 maybe configured to provide text messaging functionality to client device102. For example, user may access SMS client 122 to communicate withother client devices over network 105 via text message. VoIP client 124may be configured to provide voice communications over IP networks(e.g., network 105).

Organization computing system 106 may be associated with or controlledby an entity, such as a business, corporation, individual partnership,or any other entity that provides one or more of goods, services, andconsultations to individuals such as customers.

Organization computing system 106 may include one or more servers andcomputer systems for performing one or more functions associated withproducts and/or services provided by the organization associated withorganization computing system 106. Organization computing system 106 mayinclude a web server 108, a transaction server 109, a call center server110, a machine learning module (MLM) 112, an application programminginterface (API) server 114, a prediction device 115, a natural languageprocessing (NLP) device 116, and a dialogue management device 118communicating via local network 125.

Web server 108 may include a computer system configured to generate andprovide one or more websites accessible to users or customers, as wellas any other individuals involved in organization computer system's 106normal operations. For example, web server 108 may include a computersystem configured to receive communications from a client device 102 viaa mobile application, a chat program, an instant messaging program, avoice-to-text program, an SMS message, email, or any other type orformat of written or electronic communication. In particular, web server108 may include a computer system configured to receive communicationsfrom a client device 102 via, for example, application 120, SMS client122, or VoIP client 124, executing on client device 102.

Web server 108 may have one or more processors 125 and one or more webserver databases 126, which may be any suitable repository of websitedata. Information stored in web server 108 may be accessed (e.g.,retrieved, updated, and/or added to) via local network 125 by one ormore components of organization computing system 106. In someembodiments, processor 125 may be used to implement an automated naturallanguage dialogue system that may interact with a customer via differenttypes of communication channels, such as a website, mobile application,instant messaging application, SMS message, email, or any other type ofelectronic communication. For example, while one or more components oforganization computing system 106 monitors a user's activity via, forexample, application 120, SMS client 122, or VoIP client 124, arespective component of organization computing system 106 (e.g.,prediction device 115) may flag one or more actions performed by thecustomer and determine an anticipated user action from the flagged oneor more actions.

Call center server 110 may include a computer system configured toreceive, process, and route telephone calls and other electroniccommunications between a customer operation client device 102 anddialogue management device 118. Call center server 110 may include oneor more processors 144 and one or more call center databases 146, whichmay be any suitable repository of call center data. Information storedin call center server 110 may be accessed (e.g., retrieved, updated,and/or added to) via local network 125 by one or more devices (e.g.,dialogue management device 118) of organization computing system 106. Insome embodiments, call center server processor 144 may be used toimplement an interactive voice response (IVR) system that interacts withthe customer over the phone.

Transaction server 109 may include a computer system configured toprocess one or more transactions involving an account associated withcustomers, or a request received from customers. In some embodiments,transactions may include, for example, a product/service purchase, aproduct/service return, financial transfer, financial deposit, financialwithdrawal, financial credit, financial debit, dispute request, warrantycoverage request, account balance request, and any other type oftransaction associated with the products and/or services that an entityassociated with organization computing system 106 provides toindividuals, such as customers. Transaction server 109 may include oneor more processors 128 and one or more transaction server databases 130,which may be any suitable repository of transaction data. Informationstored in transaction server 109 may be accessed (e.g., retrieved,updated, added) via local network 125 by one or more devices oforganization computing system 106.

Prediction device 115 may include one or more computer systemsconfigured to receive one or more streams of customer (or user) activitywhile navigating the customer's account, and generate one or moreproposed solutions to an anticipated user action based on the customer'sprevious history. Prediction device 115 may compile information from aplurality of sources, such as web server 108, call center server 110,and transaction server 109. Prediction device 115 may store customeractivity in database 148. Prediction device 115 may include a predictionmodel that is used to generate the anticipated user action based onhistorical information associated with the user. Based on theanticipated user action, prediction device 115 may generate a proposedsolution that addresses the anticipated user action.

Prediction device 115 may be used to make more readily available highercost communication channels to customers with more complex questions orissues. In one example, prediction device 115 may be used to generate apre-emptive notification to be sent to the customer in anticipation ofthe customer calling into the call center (i.e., using a higher costchannel). Prediction device 115 may, for example, identify that thecustomer has previously contacted the call center on numerous occasionsto confirm that an online payment has cleared the customer's account.Prediction device 115 may identify the online payment as a “triggerevent” for the customer to call into the call center. Prediction device115 may also identify the context of the communication (e.g., checkingwhether the online payment cleared the customer's account). Based offthe identified trigger and the context of the communication, predictiondevice 115 may generate a trigger rule that transmits apush-notification to a client device of the customer notifying thecustomer that the online payment has cleared the customer's account assoon as the online payment cleared the account. Such preemptivemessaging by prediction device 115 steers the customer away from thehigher cost channel, thereby freeing up resources for customers withmore complex questions/issues.

In some embodiments, prediction model may also be based on historicalinformation associated with other customers (or users) of organizationcomputer system 106. In another example, prediction device 115 may beused to identify a trigger across multiple (e.g., all) users. In otherwords, prediction device 115 may generate a “global trigger rule” basedoff historical information associated with all customers of organizationcomputer system 106. For example, prediction device 115 may identifythat a plurality of customers typically call the call center to changetheir billing address following a transaction with a moving company.Prediction device 115 may identify the transaction with the movingcompany as a “trigger event” for the customer to call into the callcenter. Prediction device 115 may also identify the contexts of thecommunication (e.g., change the customer's billing address). Becauseprediction device 115 has recognized this pattern across a plurality ofcustomers, prediction device 115 may generate a “global trigger rule”that transmits an email message to the customer with instructions on howto update customer's billing address, following a transaction with amoving company posted to the customer's account.

For example, a user of client device 102 may perform a set of actionsvia application 120. For this example, assume the set of actionsperformed via application 120 is the user of client device 102 paying acredit card bill electronically. Prediction device 115 may determinethat the set of actions performed via application 120 is similar to pastsets of actions performed by the user via application 120. Based on thishistory, prediction device 115 may identify an anticipated actionperformed by the user following this set of actions using the predictionmodule. For example, assume that following a credit card payment, theuser almost always calls the financial organization within three daysfollowing the electronic payment to confirm that the payment wasreceived. As such, the anticipated user action in the present scenariois the subsequent phone call from the user. Based on the identifiedanticipated user action, prediction device 115 may generate a proposedsolution. Continuing with the example, prediction device 115 maygenerate a proposed solution that involves a pre-generated voicemailsent to client device 102 that confirms the user's payment. In anotherexample, production device 115 may generate a proposed solution thatinvolves a pre-generated text message sent to client device 102 thatconfirms the user's payment. In another example, production device 115may generate an electronic message (i.e., email) that confirms theuser's payment.

Machine learning module 112 may include one or more computer systemsconfigured to train a prediction model used by prediction device 115. Totrain the prediction model, machine learning module 112 may receive, asinput, one or more streams of user activity. The one or more streams ofuser activity may correspond to actions taken by the user with respectto the user's accounts with organization computing system 106. Suchstreams of activity may include payment of accounts, transferring offunds, navigation of web pages, calls to customer service, chat sessionswith a bot, interactions with emails from organization computing system106, and the like. In some embodiments, machine learning module 112 mayfurther receiver, as input, one or more streams of activity associatedwith additional users. As such, machine learning module 112 may leverageboth user specific and user agnostic information to identify bothindividualized patterns of activity and patterns of activity across allusers. Machine learning module 112 may implement one or more machinelearning algorithms to train the prediction model. For example, machinelearning module 112 may use one or more of a decision tree learningmodel, association rule learning model, artificial neural network model,deep learning model, inductive logic programming model, support vectormachine model, clustering mode, Bayesian network model, reinforcementlearning model, representational learning model, similarity and metriclearning model, rule based machine learning model, and the like. Machinelearning module 112 may include one or more processors 152 and one ormore machine learning model databases 154, which may be any suitablerepository of transaction data. Information stored in machine learningmodule 112 may be accessed (e.g., retrieved, updated, added) via localnetwork 125 by one or more devices of organization computing system 106.

Dialogue management device 118 may include one or more computer systemsconfigured to receive and/or compile information from a plurality ofsources, such as prediction device 115, web server 108, call centerserver 110, and transaction server 109, correlate received and/orcompiled information, analyze the compiled information, arrange thecompiled data, generate derived information based on the compiledinformation, and store the compiled and derived information in adatabase (e.g., database 150). According to some embodiments, database150 may be a database associated with the organization of organizationcomputing system 106 and/or its related entity that stores a variety ofinformation relating to customers, transactions, and businessoperations. Database 150 may also serve as a back-up storage device andmay contain data and information that is also stored on, for example,databases 126, 130, 136, 140, 146, 148, and 154. Database 150 may beaccessed by dialogue management device 118. For example, database 150may be used to store records of every interaction, communication, and/ortransaction a particular customer has with organization computing system106 and/or its related entities. Such record storing aids in creating anever-evolving customer context that allows dialogue management device118 to provide customized and adaptive dialogue when interacting withthe customer.

API server 114 may include a computer system configured to execute oneor more APIs that provide various functionalities related to theoperations of organization computing system 106. In some embodiments,API server 114 may include an API adapter that allows API server 114 tointerface with and utilize enterprise APIs maintained by organizationcomputing system 106 and/or an associated entity that may be housed onother systems or devices. In some embodiments, APIs may enable functionsthat include, for example, retrieving customer account information,modifying customer account information, executing a transaction relatedto an account, scheduling a payment, authenticating a customer, updatinga customer account to opt-in or opt-out of notifications, and any othersuch function related to management of customer profiles and accounts.API server 114 may include one or more processors 134 and one or moreAPI databases 136, which may be any suitable repository of APIinformation. Information stored in API server 114 may be accessed vialocal network 125 by one or more components of organization computingsystem 106. In some embodiments, API processor 134 may be configured toaccess, modify, and retrieve customer account information.

Natural language processing (NLP) device 116 may include a computersystem configured to receive and process incoming dialogue messages anddetermine a meaning of the incoming dialogue message. NLP device 116 maybe configured to receive and execute a command that includes an incomingdialogue message where the command instructs NLP device 116 to determinethe meaning of the incoming dialogue message. NLP device 116 may beconfigured to continuously monitor or intermittently listen for andreceive commands from a command queue to determine if there are any newcommands directed to NLP device 116. Upon receiving and processing anincoming dialogue message, NLP device 116 may output the meaning of anincoming dialogue message in a format that other devices can process.

In one example, the received dialogue message may be the result of auser providing additional information to organization computing system106 as a result of being prompted by organization computing system 106.In this example, organization computing system 106 may receive a textmessage from the user, which may initiate a dialogue or be part of anongoing dialogue with organization computing system 106. For example,organization computing system 106 may receive a dialogue message inresponse to a clarification question submitted by organization computingdevice. Dialogue management system 118 may place the dialogue message ina command queue, accessible by NLP device 116. NLP device 116 mayidentify that a new command is directed to NLP device 116, and maygenerate a response to the user's dialogue message. For example, NLPdevice 116 may generate a message to be transmitted to user, to invitethe user to supply further information.

NLP device 116 may include one or more processors 142 and one or moreNLP databases 140. Information stored in NLP device 116 may be accessedvia local network 125 by one or more components of organizationcomputing system 106. In some embodiments, NLP processor 142 may be usedto implement an NLP system that may determine the meaning behind astring of text and convert it to a form that can be understood by otherdevices.

Local network 125 may comprise any type of computer networkingarrangement used to exchange data in a localized area, such as Wi-Fi,Bluetooth™, Ethernet, and other suitable network connections that enablecomponents of organization computing system 106 to interact with oneanother and to connect to network 125 for interacting with components incomputing environment 100. In some embodiments, local network 125 mayinclude an interface for communicating with or linking to network 105.In some embodiments, components of organization computing system 106 maycommunicate via network 105, without a separate local network 125.

FIG. 2 is a block diagram illustrating a computing environment 200,according to an example embodiment. As illustrated, computingenvironment 200 may include at least dialogue management device 118 andprediction device 115 communicating via network 125.

Dialogue management device 118 may include a processor 204, a memory206, a storage 208, and a network interface 210. In some embodiments,dialogue management device 118 may be coupled to one or more I/Odevice(s) 212.

In some embodiments, dialogue management device 118 may be incommunication with database 150. Database 150 may store information toenable dialogue management device 118 to perform one or more of theprocesses and functionalities associated with the disclosed embodiments.Database 150 may include stored data relating to a customer profile andcustomer accounts, such as, for example, customer identificationinformation (e.g., name, age, sex, birthday, address, VIP status, keycustomer status, preferences, preferred language, vehicle(s) owned,greeting name, channel, talking points, bank accounts, mortgage loanaccounts, car loan accounts, account numbers, authorized usersassociated with one or more accounts, account balances, account paymentinformation, and any other information that may be related to a user'saccount). Database 150 may store customer interaction data that includesrecords of previous customer service interactions with a customer via awebsite, SMS message, a chat program, a mobile application, an IVRsystem, or notations taken after speaking with a customer service agent.Database 150 may also include information about business transactionsbetween organization computing system (or its related entities) and acustomer that may be obtained from transaction server 109. Database 150may also include customer feedback data, such as an indication ofwhether an automated interaction with a customer was successful, onlinesurveys filled out by a customer, surveys answered by a customerfollowing previous interaction to the company, digital feedback providedthrough websites or mobile application associated with the organization,and the like.

Processor 204 may include one or more of a microprocessor,microcontroller, digital processor, co-processor, or the like, orcombinations thereof executing stored instructions and operating uponstored data. Processor 204 is included to be representative of a singleprocessor, multiple processors, a single processor having multipleprocessing cores, and the like.

Network interface 210 may be any type of network communications enablingdialogue management device 118 to communicate externally via localnetwork 125 and/or network 105. In one example, network interface 210may allow dialogue management device 118 to communicate locally with oneor more components of organization computing system 106. In one example,network interface 210 may allow dialogue management device 118 tocommunicate externally with client device 102.

Storage 208 may be, for example, a disk storage device. Although shownas a single unit, storage 208 may be a combination of fixed and/orremovable storage devices, such as fixed disk drives, removable memorycards, optical storage, network attached storage (NAS), storage areanetwork (SAN), and the like.

Memory 206 may be representative of any suitable memory device such asvolatile or non-volatile memory, random access memory (RAM), read onlymemory (ROM), and the like. Memory 206 may include instructions thatenable processor 204 to execute one or more applications, such as serverapplications, network communication processes, and the like. Memory 206may include programming instructions 215, operating system 216, eventqueue 218, and command queue 220.

Event queue 218 may be configured to temporarily store queued events.Command queue 220 may be configured to temporarily store queuedcommands. Processor 204 may receive events from event queue 218, and inresponse to processing the event, generate one or more commands to beoutput to command queue 220. In some embodiments, dialogue managementdevice 118 may place commands in command queue 220 in an order they aregenerated. Each command may be designated to be executed by one or moredevices, such as, for example, web server 108, call center server 110,transaction server 109, API server 114, and/or NLP device 116. Each suchdevice may continuously or intermittently monitor command queue 220 todetect one or more commands that are designated to be executed by themonitoring device, and may access pertinent commands. Event queue 218may receive one or more events from other devices, such as, for example,prediction device 115, client device 102, web server 108, call centerserver 110, transaction server 109, API server 114, and/or NLP device116.

While dialogue management device 118 has been described as one form forimplementing one or more techniques described herein, those havingordinary skill in the art will appreciate that other, functionallyequivalent techniques may be employed.

Prediction device 115 may include a processor 254, a memory 256, astorage 258, and a network interface 260. In some embodiments,prediction device 115 may be coupled to one or more I/O device(s) 262.

In some embodiments, prediction device 115 may be in communication withdatabase 148. Database 148 may store information to enable predictiondevice 115 to perform one or more of the processes and functionalitiesassociated with the disclosed embodiments. Database 148 may includestored data relating to a customer profile and customer accounts, suchas, for example, customer identification information (e.g., name, age,sex, birthday, address, VIP status, key customer status, preferences,preferred language, vehicle(s) owned, greeting name, channel, talkingpoints, bank accounts, mortgage loan accounts, car loan accounts,account numbers, authorized users associated with one or more accounts,account balances, account payment information, and any other informationthat may be related to a user's account). Database 148 may storecustomer interaction data that includes records of previous customerservice interactions with a customer via a website, SMS message, a chatprogram, a mobile application, an IVR system, or notations taken afterspeaking with a customer service agent. Database 148 may also includehistorical information about user activity on web pages hosted by webserver 108 of organization computing system 106. For example, database148 may identify patterns in user behavior as the user navigates throughwebsites of organization computing system 106.

Processor 254 may include one or more of a microprocessor,microcontroller, digital processor, co-processor, or the like, orcombinations thereof executing stored instructions and operating uponstored data. Processor 254 is included to be representative of a singleprocessor, multiple processors, a single processor having multipleprocessing cores, and the like.

Network interface 260 may be any type of network communications enablingprediction device 115 to communicate externally via local network 125and/or network 105. In one example, network interface 260 may allowprediction device 115 to communicate locally with one or more componentsof organization computing system 106. In one example, network interface260 may allow prediction device 115 to communicate with dialoguemanagement device 118.

Storage 258 may be, for example, a disk storage device. Although shownas a single unit, storage 258 may be a combination of fixed and/orremovable storage devices, such as fixed disk drives, removable memorycards, optical storage, network attached storage (NAS), storage areanetwork (SAN), and the like.

Memory 256 may be representative of any suitable memory device such asvolatile or non-volatile memory, random access memory (RAM), read onlymemory (ROM), and the like. Memory 256 may include instructions thatenable processor 254 to execute one or more applications, such as serverapplications, network communication processes, and the like. Memory 256may include programming instructions 265, operating system 266,prediction model 268, and event generator 270.

Programming instructions 265 may be accessed by processor 254 forprocessing (i.e., executing programming instructions). Programminginstructions 265 may include, for example, executable instructions forcommunicating with dialogue management device 118. For example,programming instructions 265 may include executable instructionsconfigured to perform steps discussed below in conjunction with FIGS. 4,6, and 7.

Prediction model 268 may be accessed by processor 254 for generating ananticipated user action based on one or more current actions taken bythe user. For example, processor 254 may input one or more streams ofuser activity into prediction model 268, such that prediction model 268may determine an anticipated user action based on the output fromprediction model 268. Prediction model 268 may be trained using machinelearning module 112. For example, machine learning module 112 may trainprediction model 268 using one or more streams of historical useractivity. The one or more streams of historical user activity maycorrespond to actions taken by the user with respect to the user'saccounts. For example, machine learning module may input one or morestreams of historical user data according to a communication channel inwhich the user corresponded with organization computing system 106. Insome embodiments, prediction model 268 may further be trained with oneor more streams of historical activity of additional users oforganization computing system 106.

Prediction model 268 may be used to generate one or more “triggerevents” both on an individualized, customer basis, as well as a globalbasis (i.e., across all customers). For example, prediction model 268may receive on or more streams of customer activity. Based on the one ormore streams of customer activity, prediction model 268 may determinethat the customer typically calls into the call center five days beforean account payment is due to make the payment over the phone. Predictionmodel 268 may identify the fifth day from the payment due date as a“trigger event” for the customer. Prediction model 268 may also identifythe account payment as context of the communication. Based off theidentified trigger event and the context, prediction model 268 mayoutput a trigger rule that transmits an email to the customer, with alink to an online payment page.

Prediction model 268 may generate a trigger rule on a global basis basedoff of identified patterns in the activity of a plurality of customers.For example, upon identifying that customers, who typically make accountpayment over the phone, tend to wait until the last possible day to paytheir account balance, prediction model 268 may generate a trigger rulein which the user receives a call from an artificial-intelligence basedcustomer service channel, reminding the customer of the payment, as wellas prompting the user to submit a payment at this time.

Event generator 270 may be configured to generate an event correspondingto the anticipated user action that was determined by prediction model268. In other words, event generator 270 may be configured to generate aproposed solution to the anticipated user action and, subsequently,event generator 270 may add the proposed solution to event queue 218 forfurther processing by dialogue management device 118. For example,assuming prediction model 268 determines, based on the one or morestreams of user activity, that the user is likely to call arepresentative of organization computing system 106 to verify that aprior credit card payment was received and credited, event generator 270may generate a proposed solution of sending the user a text messageincluding confirmation of the payment. Event generator 270 may add thisproposed solution to event queue 218 as an event to be processed bydialogue management device 118 and other components of organizationcomputing system 106.

While prediction device 115 has been described as one form forimplementing one or more techniques described herein, those havingordinary skill in the art will appreciate that other, functionallyequivalent techniques may be employed.

FIG. 3 is a block diagram 300 illustrating one or more communicationchannels between client device 102 and organization computing system106, according to one embodiment. As illustrated, one or morecommunication channels may include channel 1 customer activity 302,channel 2 customer activity 304, and channel 3 customer activity 306.Channel 1 customer activity 302 may correspond to customer activity viatelecommunication means (i.e., the customer calling a live customerservice channel associated with organization computing system 106).

Generally, Channel 1 communication may be a considered a “higher cost”communication channel compared to Channel 2 communication and Channel 3communication. Higher cost may refer to a relatively higher outlay onpersonnel and other related call center resources. For some enterprises,Channel 1 communication may be encouraged for complex issues, higherdemand, higher sensitive means of communication between client device102 and organization computing system 106. As such, limiting the trafficof Channel 1 communication aids in increasing the bandwidth betweenclient device 102 and organization computing system 106 for those morecritical customer issues.

Channel 2 and Channel 3 communications may be considered lower costscompared to Channel 1 communications. Channel 2 customer activity 304may correspond to customer activity via automated telecommunicationmeans (i.e., the customer calling an artificial-intelligence basedcustomer service channel). Channel 3 customer activity 306 maycorrespond to customer activity via a computer network (i.e., via textmessaging, electronic messages (email), and the like).

Each of Channel 1 customer activity 302, Channel 2 customer activity304, and Channel 3 customer activity 306 may be inputted into machinelearning module 112 to train predictive model 268. In particular,machine learning module 112 may be provided with a training set ofhistorical user activity that is categorized on a communication channelbasis. As such, machine learning module 112 may not only trainprediction model 268 to determine an anticipated user action, but toalso identify the communication in which the user is most likely to use,and determine whether a lower cost channel may be used to address theanticipated user action.

Once predictive model 268 is trained, live customer activity may beinput directly into prediction model 268.

FIG. 4 is a flow diagram illustrating an exemplary method 400 ofgenerating an intervening message for a remote client device in responseto an anticipated user action, according to one embodiment. Method 400begins at step 402. At step 402, user may perform one or more activitiesassociated with a user account on client device 102. For example, usermay navigate one or more web pages associated with the user's account.In some embodiments, one or more activities may correspond to one ormore of payment of an account, transferring of funds, navigation to acustomer service chatbot, a search of previous transactions, a line ofcredit application, a loan application, a change of personalidentification information, and the like.

At step 404, organization computing system 106 may monitor one or moreactivities of the user. For example, prediction device 115 may monitorone or more streams of user activity. In some embodiments, predictiondevice 115 may monitor one or more streams of user activity while theuser is accessing the user's account. In some embodiments, predictiondevice 115 may monitor one or more streams of user activity while theuser is accessing a web page hosted by web server 108. In someembodiments, prediction device 115 may monitor one or more streams ofuser activity while the user is navigating web pages associated with oneor more third party servers 104.

At step 406, organization computing system 106 may input the one or morestreams of user activity into prediction model 268. For example,prediction device 115 may input the one or more streams of user activityinto prediction model 268 to generate an anticipated user action.

At step 408, organization computing system 106 may determine ananticipated user action based on the one or more streams of useractivity. For example, after receiving the one or more streams of useractivity as input, prediction model 268 may generate an output. Based onthe output, prediction device 115 may determine an anticipated useraction. In some embodiments, prediction device 115 may determine one ormore anticipated user actions. Prediction device 115 may select a givenanticipated user action by determining the anticipated user action thatis most probable. In some embodiments, prediction device 115 may furtherdetermine the communication channel in which the anticipated user actionwill occur. For example, based on output from prediction model 268,prediction device 115 may determine that user is likely to contactorganization computing system 106 via a live operator (e.g., Channel 1communication).

At step 410, organization computing system 106 may determine a proposedsolution based on the determined anticipated user action. For example,prediction device 115 may determine the proposed course of action totake based on what the prediction device 115 predicted the user's nextaction may be. In some embodiments, the proposed solution may take intoaccount the complexity of the anticipated user action. For example, inthose situations where the anticipated user action is relatively complex(e.g., transferring large sums of money, applying for a mortgage, etc.),prediction device 115 may determine that the proposed course of actionis to reach out to the user via a live operator (e.g., Channel 1communication). In another example, such as those situations where theanticipated user action is not relatively complex (e.g., calling a liveoperator to confirm a payment cleared), prediction device 115 maydetermine that the proposed solution is to anticipate the user's call,and use a lower cost channel (e.g., text message notification) to notifythe user of payment confirmation.

At step 412, organization computing system 106 may generate ananticipated message to be transmitted to client device 102. For example,dialogue management system 118 may work in conjunction with one or morecomponents of organization computing system 106 and a communicationsinterface (illustrated in FIGS. 5A and 5B) to generate a message thatincludes an indication of the proposed solution.

At step 414, organization computing system 106 may transmit theanticipated message to client device 102. For example, organizationcomputing system 106 may transmit the anticipated message to clientdevice 102 via a communication channel determined above, in conjunctionwith step 410.

At step 416, client device 102 may receive the anticipated message fromorganization computing system 106. In some embodiments, in which theanticipated message is transmitted via a Channel 1 communicationchannel, client device 102 may receive a phone call from a liveoperator. In some embodiments, in which the anticipated message istransmitted via a Channel 2 communication channel, client device 102 mayreceive a phone call from an AI-based customer service channel. In someembodiments, in which the anticipated message is transmitted via aChannel 3 communication channel, client device 102 may receive theanticipated message via email via application 120, text message via SMSagent 122, push notification via application 120, and the like.

In some embodiments, the anticipated message may be received via SMSagent 122 from an interactive chatbot. The interactive chatbot mayestablish a persistent session to communicate customer specificinformation with client device 102. Additionally, interactive chatbotmay engage in dialogue with user (or customer) of client device 102,such that the chatbot can respond to any follow-up questions thecustomer may have. For example, a user or customer may be enrolled orpre-registered (or otherwise configured) to communicate with anassistant (live or automated) via an SMS channel, as similarly describedin U.S. patent application Ser. No. 15/916,521 filed Mar. 9, 2018,titled “Systems and Methods for Controlling Enrollment and SecurePersistent SMS Texting Account Servicing with an Intelligent Assistant,”the entirety of the contents of which are expressly incorporated herein.

In some embodiments, a chat session may be initiated with the customervia an email from organization computing system 106 to client device102. For example, a user or customer may receive the anticipated messagevia an email that includes one or more predefined hyperlinks that areactionable by the user, as similarly described in U.S. patentapplication Ser. No. 15/987,157 filed May 23, 2018, titled “Method andSystem of Converting Email Message to AI Chat,” the entirety of thecontents of which are expressly incorporated herein. Selection of one ormore hyperlinks may initiate a dialogue with a virtual assistant via anSMS channel. In some embodiments, where the user or customer may not yethave consented to communicate with text or may not otherwise be enrolledor registered to communication with an assistant via an SMS channel, theemail transmitted to user with the one or more hyperlinks includedtherein includes a request, consent or authorization to enable theorganization to contact or interact with the user via text message. Forexample, the email transmitted to the user may include a statement thatselecting one of the hyperlinks corresponds to the user implicitlygranting organization computing system 106 authorization to contact uservia text message.

In some embodiments, organization computing system 106 may initiate aweb based chat using dialogue management device 118. For example,organization computing system 106 may detect that a user is on a website hosted by web server 108. Based on this determination, dialoguemanagement device 118 may initiate an interactive chat session with thecustomer while the customer is browsing the web site. In anotherexample, organization computing system 106 may make available fordownload a browser extension. When customer adds the browser extensionto a browser (e.g., application 120), dialogue management device 118 mayinitiate an interactive chat session with the customer while thecustomer is online. In this example, the customer need not be navigatinga web site hosted by web server 108.

FIG. 5A is a block diagram illustrating an exemplary systemfunctionality diagram 500 for a system generating an intervening messagefor a remote client device in response to an anticipated user action,according to one embodiment. The functionality illustrated in FIG. 5Amay be executed by one or more components of computing system 100. FIG.6 is a flow diagram illustrating a method 600 for generating anintervening message for a remote client device in response to ananticipated user action, according to one embodiment. The one or moresteps discussed in conjunction with FIG. 6 may correspond to the systemfunctionality diagram 500 illustrated in FIG. 5A.

As illustrated in FIG. 5A, prediction device 115 may receive one or morestreams of user activity from client device 102 at block 502 (e.g.,block 602 of FIG. 6). In some embodiments, prediction device 115 maymonitor one or more streams of user activity while the user is accessingthe user's account. In some embodiments, prediction device 115 maymonitor one or more streams of user activity while the user is accessinga web page hosted by web server 108. In some embodiments, predictiondevice 115 may monitor one or more streams of user activity while theuser is navigating web pages associated with one or more third partyservers 104.

As shown in FIGS. 5A and 6, at blocks 504 and 604, organizationcomputing system 106 may generate a first event to be placed in eventqueue 218. For example, prediction device 115 may generate a first eventby inputting the one or more streams of user activity into predictionmodel 268 to determine an anticipated user action based on the useractivity. Based on output from prediction model 268, prediction device115 may anticipate a user action. Additionally, prediction device 115may specify the communication channel in which the anticipated useraction is most likely to occur. For example, prediction device 115 maydetermine that the user is likely to call a live customer servicerepresentative around mid-month to determine when a credit card paymentis due. Accordingly, prediction device 115 determines that anticipateduser action (e.g., seeking credit card payment date mid-month) and thecommunication channel in which the user performs the action (e.g.,telephone call to live customer service representative).

From the anticipated user action, prediction device 115 may furtherdetermine a proposed solution. For example, prediction device 115 maydetermine the proposed course of action to take based on what theprediction device 115 predicted the user's next action is likely to be.In some embodiments, the proposed solution may take into account thecomplexity of the anticipated user action. Using the proposed solution,prediction device 115 may generate a first event to be placed in eventqueue 218. Continuing with the above example, the event to be placed inevent queue may be “contact user via lower cost channel to notify userof credit card payment date on the 14^(th) of every month”). The eventmay further include user-specific information, such that dialoguemanagement device 118 may readily identify the user and the user'saccounts in subsequent processing. As such, prediction device 115 mayinject a user identifier in the event that corresponds to the user. Suchuser identifier may be, for example, a unique set of characterscorresponding to the user. After the first event is created, predictiondevice 115 may place the first event in event queue 218. For example,event queue 218 may be configured to temporarily store one or moreevents for subsequent processing by dialogue management device 118.

As illustrated above in conjunction with FIG. 2, event queue 218 andcommand queue 220 may be one or more components of dialogue managementdevice 118. In some embodiments, both event queue 218 and command queue220 may be one or more components of a device other than dialoguemanagement device 118. For example, those skilled in the art willappreciate that event queue 218 and command queue 220 may be one or morecomponents maintained on a cloud server, accessible by, for example,dialogue management device 118.

In some embodiments, dialogue management device 118 may continuously (orintermittently) monitor event queue 218 for events posted therein. Atblocks 506 (FIG. 5A) and 606 (FIG. 6), in response to detecting an event(e.g., the first event) in event queue 218, dialogue management device118 may receive the event from event queue 218. In some embodiments, theuser context may be derived using the user identifier included in thefirst event. For example, dialogue management device 118 may associatethe user identifier with a given user and their user information that isstored in database 150. In some embodiments, the user information mayinclude one or more of account types, account statuses, transactionhistory, conversation history, people models, an estimate of usersentiment, user goals, user social media information, and the like. Theuser context may allow organization computing system 106 to adapt andtailor its responses to a particular customer based on the identifieduser context. In some embodiments, the user context may be updated eachtime dialogue management device 118 receives a new event from eventqueue 218.

At blocks 508 (FIG. 5A) and 608 (FIG. 6), dialogue management device 118may generate a first command to be placed in command queue 220. In someembodiments, dialogue management device 118 may generate a command basedon the processed event, the customer context, and the proposed solution.In some embodiments, upon generating a command, dialogue managementdevice 118 may identify a component that may subsequently execute thecommand. For example, dialogue management device 118 may determinewhether one or more of API server 114, NLP device 116, or communicationinterface 501 may subsequently execute the command.

At blocks 510, 512 (FIG. 5A) and 610 (FIG. 6), API server 114 mayreceive the first command from command queue 220, execute the firstcommand, and generate a second event to be placed in event queue 218. Insome embodiments, API server 114 may continuously or intermittentlymonitor command queue 220 to detect new commands. Upon receiving acommand, API server 114 may perform one or more functions associatedwith the command. For example, based on the proposed solution containedin the command, API server 114 may call up an API stored locally orremotely on another device to retrieve user account information (e.g.,retrieve account balance, retrieve payment date, etc.), perform anaccount action (e.g., make a payment on a customer account),authenticate a customer (e.g., verify customer credentials), and thelike. Accordingly, in some embodiments, the second event may represent aretrieved account balance, an acknowledgement of the performance of anaccount action, etc. Generally, the second event may correspond toorganization computing system 106 carrying out an anticipated inquiry(or request) from the user.

At blocks 514 (FIG. 5A) and 612 (FIG. 6), dialogue management device 118may receive the second event from event queue 218 in response todetecting the second event placed therein. At blocks 516 (FIG. 5A) and614 (FIG. 6), dialogue management device 118 may, in response toprocessing the second event, generate a second command to be placed incommand queue 220. In some embodiments, dialogue management device 118may generate the second command based on the processed second event andthe user context.

At blocks 518 and 520, communication interface 501 may receive andexecute the second command, which may cause communication interface 501to transmit an anticipated message to client device 102 fromorganization computing system 106. For example, executing the secondcommand may cause communication interface to transmit (via text message)a response dialogue to client device 102 that provides an indication ofthe proposed solution. Continuing with the above example, the indicationmay be the date upon which the user's credit card payment is due. Stillfurther, the message is transmitted to client device 102 via a lowercost communication channel (e.g., text message) compared to the user'straditional methods of contacting organization (e.g., live telephoneoperator).

In another example, in response to anticipating the user making apayment to pay off the entire credit card balance (based on the user'shistory), the indication may be a question to the user comprising:“Would you like to make a payment of [entire balance] today?”

In some embodiments, communication interface 501 may continuously orintermittently monitor command queue 220 for new commands, and mayreceive the second command in response to detecting the second commandposted to event queue 220. In some embodiments, communication interface501 may be a standalone component having some or all elements ofdialogue management device 118, as illustrated in FIG. 2. In someembodiments, communication interface 501 may be integrated into dialoguemanagement device 118 (e.g., as an I/O device 212). In some embodiments,communication interface 501 may be integrated into another component oforganization computing system 106, such as, for example, web server 108,call center server 110, transaction server 109, API server 114,prediction device 115, or NLP server 116.

FIG. 5B is a block diagram illustrating an exemplary systemfunctionality diagram 550 for a system generating an intervening messagefor a remote client device in response to an anticipated user action,according to one embodiment. The functionality illustrated in FIG. 5Bmay be executed by one or more components of computing system 100. FIG.7 is a flow diagram illustrating a method 700 for generating anintervening message for a remote client device in response to ananticipated user action, according to one embodiment. The one or moresteps discussed in conjunction with FIG. 7 may correspond to the systemfunctionality diagram 550 illustrated in FIG. 5B.

Method 700 may begin upon receiving a further prompt from client device102. Referring back to step 614 of FIG. 6, organization computing system106 may generate and send a message to client device 102 seeking furtherinput from the user. Continuing with a previous example, upondetermining that an anticipated user action is to pay the entire balanceof an account, organization computing system 106 may transmit a messageto client device 102 with an indication of a proposed solution—“Wouldyou like us to pay the balance [$XX.XX] on your credit card statement?”

At block 552, communication interface 551 may receive an incomingdialogue message from client device 102. Incoming dialogue message maybe received in response to the user receiving the anticipated messagewith an indication of the proposed solution. In response, user maygenerate a dialogue message that is transmitted from client device 102to organization computing system 106. In some embodiments, the incomingdialogue message from client device 102 may be transmitted via a samecommunication channel as the outgoing anticipated message. For example,the anticipated message was transmitted to client device 102 via textmessage; similarly, the incoming dialogue message was received fromclient device 102 via text message. In some embodiments, the incomingdialogue message from client device 102 may be transmitted via adifferent communication channel. For example, the anticipated messagewas transmitted to client device 102 via email; the incoming dialoguemessage was received from client device 102 via text message.

At blocks 554 (FIG. 5B), and 702 (FIG. 7), organization computing system106 may generate a third event to be placed in event queue 218 inresponse to receiving a customer dialogue message. In some embodiments,the customer dialogue message may be sent directly from client device102 to event queue 218. In some embodiments, the customer dialoguemessage may be transmitted to dialogue management device 118, andsubsequently transmitted from dialogue management device 118 to eventqueue 218. The customer dialogue message may include informationassociated with the anticipated message transmitted from communicationinterface 501 to client device 102.

In some embodiments, dialogue management device 118 may continuously orintermittently monitor event queue 218. At blocks 556 (FIG. 5B) and 704(FIG. 7) in response to detecting an event (e.g., the third event) inevent queue 218, the event may be received at dialogue management device118. At blocks 558 (FIG. 5B) and 706 (FIG. 7), dialogue managementdevice 118 may, in response to processing the third event, generate athird command to be placed in command queue 220. In some embodiments,dialogue management device 118 may generate the command based on theprocessed event, the message received from the client device 102, andcustomer context. In some embodiments, when dialogue management device118 generates a command, such as the third command, dialogue managementdevice 118 may determine an entity that will execute the command. Forexample, in the embodiment discussed in conjunction with FIGS. 5B and 7,dialogue management device 118 may determine that the third command isto be executed by NLP device 116 in order to determine the meaning ofincoming customer dialogue message.

At blocks 560 (FIG. 5B) and 708 (FIG. 7), NLP device 116 may receive thethird command from command queue 220. According to some embodiments, NLPdevice 116 may continuously or intermittently monitor command queue 220to detect new commands and, upon detecting a new command, may receivethe command from command queue 220. Upon receiving a command, NLP device116 may perform various functions, depending on the nature of thecommand. For example, in some embodiments, NLP device 116 may determinethe meaning of an incoming dialogue message by utilizing one or moreartificial intelligence techniques. Such artificial intelligencetechniques may include, but are not limited to, intent classification,named entity recognition, sentiment analysis, relation extraction,semantic role labeling, question analysis, rules extraction anddiscovery, and story understanding.

In some embodiments, NLP device 116 may perform natural languagegeneration in response to receiving a command. In some embodiments, NLPdevice 116 may perform natural language processing by utilizing one ormore artificial intelligence techniques. Such artificial intelligencetechniques may include, but are not limited to, content determination,discourse structuring, referring expression generation, lexicalization,linguistic realization, explanation generation. In the exemplaryembodiment discussed in conjunction with FIGS. 5B and 7, NLP device 116may determine the meaning of the incoming customer dialogue message andconvert it to a form that may be processed by dialogue management device118. Accordingly, dialogue management device 118 may generate a fourthevent that represents a determined meaning of the incoming customerdialogue message. NLP device 124 may transmit the fourth event to eventqueue 218 (562 of FIG. 5B; 708 of FIG. 7).

At blocks 564 (FIG. 5B) and 710 (FIG. 7), dialogue management device 118may receive the fourth event from event queue 218, in response todetecting the fourth event, according to one or more operationsdescribed above. At blocks 566 (FIG. 5B) and 712 (FIG. 7), dialoguemanagement device 118 may, in response to processing the fourth event,generate a fourth command to be placed in command queue 220. Accordingto some embodiments, dialogue management device 120 may generate thefourth command based at least on the processed event and customercontext. For example, the fourth event may represent the user's dialoguemessage in response to the clarification message. Accordingly the thirdevent may read: “Pay entire balance of the account.”

At blocks 568 (FIG. 5B) and 714 (FIG. 7), API server 114 may receive thefourth command from command queue 220, execute the command, and generatea fifth event to be placed in event queue 218 (570 of FIG. 5B).According to some embodiments, API server 114 may continuously orintermittently monitor command queue 220 to detect new commands. Upondetecting a new command in command queue 220, API server 114 may receivethe command from command queue 220. Upon receiving a command, API server114 may perform various functions, depending on the nature of thecommand. For example, in some embodiments, API server 114 may call up anAPI stored locally or remotely on another device, to retrieve customerinformation (e.g., retrieve an account balance), perform an accountaction, or execute an opt-in/opt-out command. Accordingly, continuingwith the above example, the fourth event may include the paying theentire balance of the user's account.

At blocks 572 (FIG. 5B) and 716 (FIG. 7), dialogue management device 118may receive the fifth event from event queue 218 in response todetecting the fifth event, according to one or more operations discussedabove. At blocks 574 (FIG. 5B) and 718 (FIG. 7), dialogue managementdevice 118 may, in response to processing the fifth event, generate afifth command to be placed in command queue 220. According to someembodiments, dialogue management device 118 may generate the fifthcommand based on at least the processed fifth event and the customercontext. In some embodiments, dialogue management device 118 may alsogenerate a response dialogue message in response to processing the fifthevent. In some embodiments, dialogue management device 118 may receive aresponse dialogue message (e.g., confirmation of payment) as an eventproduced by NLP device 116. In some embodiments, the fifth command mayrepresent a command or instructions to communication interface 501 totransmit the response dialogue message to client device 102.

At blocks 576 (FIG. 5B) and 578 (FIG. 5B), communication interface 501may receive and execute the fifth command, which may cause communicationinterface 501 to continue to transmit response dialogue message toclient device 102. For example, executing the fifth command may causecommunication interface 501 to transmit (via text message) a responsedialogue to client device 102. In some embodiments, communicationinterface 501 may continuously or intermittently monitor command queue220 for new commands, and may receive the fifth command in response todetecting the fifth command posted to event queue 220. The responsedialogue may include the response to the user's clarification. Forexample, in response to the confirmation to pay the entire balance ofthe account, the response dialogue message may include a confirmation ofthe payment.

FIG. 8 is a block diagram 800 illustrating a screen shot of a clientdevice 802, according to one embodiment. Client device 802 maycorrespond to client device 102. Block diagram 800 includes clientdevice 802. Client device 802 may have a screen 804. In one embodiment,captured on screen 804 is a screenshot 806 of SMS client 122. Asillustrated, SMS client 122 is displaying one or more text messages808-812 between user and organization computing system 106. Message 808corresponds to an anticipated message transmitted by organizationcomputing system 106. Prediction device 115 may have determined that theanticipated user action is to make a payment on a credit card account.The proposed solution to this action would be to make the payment.Message 808 includes an indication of this proposed solution, byprompting user to make the payment. As illustrated, message 808 includes“Hi, would you like to pay off your total balance on your credit cardaccount.” User of client device 802 may generate a response message(i.e., response dialogue) 810. The response message from the user mayinclude: “Yes, pls.” Based off this response message, organizationcomputing system 106 may make the credit card payment, and generatemessage 812 that confirms the payment with the user.

FIG. 9 is a block diagram 900 illustrating one or more screenshots of aclient device 902, according to one embodiment. Client device 902 maycorrespond to client device 102. Block diagram 900 includes clientdevice 902, which may correspond to client device 102 described above.Client device 902 has a screen 904. In one embodiment, captured onscreen 904 is a screenshot 906 of user of client device 902 navigatinguser's email account. As illustrated, screenshot 902 includes an emailmessage 908. Email message 908 may be generated and transmitted fromorganization computing system 106 to client device 902 via, for example,third party web server 104. Email message 908 may correspond to ananticipated message transmitted by organization computing system 106.Prediction device 115 may have determined that the anticipated useraction is to make a payment on one or more of user's credit cardaccounts (e.g., Account A, Account B). The proposed solution to thisaction would be to make one or more payments. Email message 908 includesan indication of this proposed solution, by prompting user to make thepayment.

As shown, email message 908 may include one or more pre-defined dialoguerequest prompts 910-916 embedded therein. Each dialogue request prompt910-916 includes an underlying hyperlink generated by organizationcomputing system 106. Each underlying hyperlink may include a useridentifier corresponding to user of client device 902 and a requestidentifier corresponding to the dialogue request prompt. Upon selectionof a dialogue request prompt 910-916 (e.g., upon selecting dialoguerequest prompt 910), client device 902 transmits an HTTP request toorganization computing system 106 for subsequent processing.

In another embodiment, captured on screen 904 is screenshot 956 ofclient device 902, executing SMS client 122. Screenshot 956 illustratesan interactive chat session established between client device 902 andorganization computing system 106. SMS client 122 may include one ormore messages 960-968. Upon selecting dialogue request prompt 910, forexample, organization computing system 106 may transmit a text message960 to client device 902 in response to user's request. As illustrated,text message 960 may include a further question directed to the user. Inthis example, the further question is “Hi, the balance on Account A is$100.00. Would you like to make a minimum payment of $25.00 or anotheramount?” In response, user, via client device 902, may submit a textmessage 962 back to organization computing system 106 that recites “Plssubmit a payment of 80.” Organization computing system 106 may confirmthe response by transmitting a text message 964 that recites “Pleaseconfirm: submit a payment of $80.00 to Account A.” Client device 902 maysubmit a text message 966 confirming the amount. In response,organization computing system 106 may confirm the payment bytransmitting a text message 968 that recites “We have submitted thepayment of $80.00. Balance for Account A is $20.00.” As such, theinitial web-based communication channel established between organizationcomputing system 106 and client device 902 has switched to a textmessage-based communication channel.

While the foregoing is directed to embodiments described herein, otherand further embodiments may be devised without departing from the basicscope thereof. For example, aspects of the present disclosure may beimplemented in hardware or software or a combination of hardware andsoftware. One embodiment described herein may be implemented as aprogram product for use with a computer system. The program(s) of theprogram product define functions of the embodiments (including themethods described herein) and can be contained on a variety ofcomputer-readable storage media. Illustrative computer-readable storagemedia include, but are not limited to: (i) non-writable storage media(e.g., read-only memory (ROM) devices within a computer, such as CD-ROMdisks readably by a CD-ROM drive, flash memory, ROM chips, or any typeof solid-state non-volatile memory) on which information is permanentlystored; and (ii) writable storage media (e.g., floppy disks within adiskette drive or hard-disk drive or any type of solid staterandom-access memory) on which alterable information is stored. Suchcomputer-readable storage media, when carrying computer-readableinstructions that direct the functions of the disclosed embodiments, areembodiments of the present disclosure.

It will be appreciated to those skilled in the art that the precedingexamples are exemplary and not limiting. It is intended that allpermutations, enhancements, equivalents, and improvements thereto areapparent to those skilled in the art upon a reading of the specificationand a study of the drawings are included within the true spirit andscope of the present disclosure. It is therefore intended that thefollowing appended claims include all such modifications, permutations,and equivalents as fall within the true spirit and scope of theseteachings.

What is claimed:
 1. A method, comprising: receiving, by a computingsystem, one or more streams of a user's activity with a server of anorganization via a remote client device; inputting, by the computingsystem, the one or more streams of user activity into a prediction modelconfigured to predict, based on the user activity, a future action to betaken by the user; determining, by the computing system and based on aprediction output from the prediction model, that the predicted futureaction corresponds to an inquiry to be made by the user via a callcenter service communication channel; determining, by the computingsystem, based on a solution model, a proposed solution to the predictedfuture action, wherein the proposed solution addresses the inquiry to bemade by the user via the call center service communication channel;determining, by the computing system, an electronic messaging channel ora text messaging channel to provide the proposed solution to the user;generating, by the computing system, a message to be transmitted to theuser via the determined messaging channel, the message comprising anindication of the proposed solution; and transmitting, by the computingsystem, the message to the user via the determined messaging channel. 2.The method of claim 1, wherein the proposed solution comprises a textualsolution via an automated chatbot.
 3. The method of claim 1, whereingenerating the message comprises embedding one or more predefinedhyperlinks into the message to be transmitted to the user, whereinselection of one of the predefined hyperlinks initiates a dialoguebetween the user and a virtual assistant.
 4. The method of claim 1,wherein generating the message comprises generating the message as apush-notification transmitted to the user.
 5. The method of claim 1,further comprising: generating, by the computing system, the predictionmodel configured to predict the future action to be taken by the user.6. The method of claim 5, further comprising: generating, by thecomputing system, a training set comprising a plurality of streams ofhistorical user activity associated with a plurality of users; andtraining, by the computer system, the prediction model to generate aplurality of predicted future actions based on the plurality of streamsof historical user activity.
 7. The method of claim 6, furthercomprising: training, by the computer system, the prediction model todetermine a communication channel each user is likely to access for eachof the plurality of predicted future actions.
 8. A non-transitorycomputer readable medium including one or more sequences of instructionsthat, when executed by one or more processors, cause a computing systemto perform operations comprising: monitoring, by the computing system, auser's activity with a server of an organization via a remote clientdevice; predicting, by the computing system using a prediction model, afuture action to be taken by the user based on the monitored useractivity; determining, by the computing system, that the predictedfuture action corresponds to an inquiry to be made by the user via acall center service communication channel; determining, by the computingsystem, based on a solution model, a proposed solution to the predictedfuture action, wherein the proposed solution addresses the inquiry to bemade by the user via the call center service communication channel;determining, by the computing system, an electronic messaging channel ora text messaging channel to provide the proposed solution to the user;generating, by the computing system, a message to be transmitted to theuser via the determined messaging channel, the message comprising anindication of the proposed solution; and transmitting, by the computingsystem, the message to the user via the determined messaging channel. 9.The non-transitory computer readable medium of claim 8, wherein theproposed solution comprises a textual solution via an automated chatbot.10. The non-transitory computer readable medium of claim 8, whereingenerating the message comprises embedding one or more predefinedhyperlinks into the message to be transmitted to the user, whereinselection of one of the predefined hyperlinks initiates a dialoguebetween the user and a virtual assistant.
 11. The non-transitorycomputer readable medium of claim 8, wherein generating the messagecomprises generating the message as a push-notification transmitted tothe user.
 12. The non-transitory computer readable medium of claim 8,further comprising: generating, by the computing system, the predictionmodel configured to predict the future action to be taken by the user.13. The non-transitory computer readable medium of claim 12, furthercomprising: generating, by the computing system, a training setcomprising a plurality of streams of historical user activity associatedwith a plurality of users; and training, by the computer system, theprediction model to generate a plurality of predicted future actionsbased on the plurality of streams of historical user activity.
 14. Thenon-transitory computer readable medium of claim 13, further comprising:training, by the computer system, the prediction model, to determine acommunication channel each user is likely to access for each predictedfuture action of the plurality of predicted future actions.
 15. A systemcomprising: one or more processors; and a memory having programminginstructions stored thereon, which, when executed by the one or moreprocessors, cause the system to perform one or more operationscomprising: receiving one or more streams of a user's activity with aserver of an organization via a remote client device; inputting the oneor more streams of user activity into a prediction model configured topredict, based on the user activity, a future action to be taken by theuser; determining, based on a prediction output from the predictionmodel, that the predicted future action corresponds to an inquiry to bemade by the user via a call center service communication channel;determining based on a solution model, a proposed solution to thepredicted future action, wherein the proposed solution addresses theinquiry to be made by the user via the call center service communicationchannel; determining an electronic messaging channel or a text messagingchannel to provide the proposed solution to the user; generating amessage to be transmitted to the user via the determined messagingchannel, the message comprising an indication of the proposed solution;and transmitting the message to the user via the determined messagingchannel.
 16. The system of claim 15, wherein the proposed solutioncomprises a textual solution via an automated chatbot.
 17. The system ofclaim 15, wherein generating the message comprises embedding one or morepredefined hyperlinks into the message to be transmitted to the user,wherein selection of one of the predefined hyperlinks initiates adialogue between the user and a virtual assistant.
 18. The system ofclaim 15, wherein generating the message comprises generating themessage as a push-notification transmitted to the user.
 19. The systemof claim 15, wherein the one or more operations further comprise:generating the prediction model configured to predict the future actionto be taken by the user.
 20. The system of claim 19, wherein the one ormore operations further comprise: generating a training set comprising aplurality of streams of historical user activity associated with aplurality of users; training the prediction model to generate aplurality of predicted future actions based on the plurality of streamsof historical user activity; and training the prediction model todetermine a communication channel each user is likely to access for eachpredicted future action of the plurality of predicted future actions.